At DSTO, Zelinsky says machine learning algorithms run unmanned aerial vehicles, unmanned underwater vehicles, unmanned surface vehicles, unmanned ground vehicles, and so on.
“One of the big applications of unmanned systems is what we call intelligence, surveillance and reconnaissance (ISR). They can be done by unmanned systems or satellites. They give you images of the environment or the planet and they have terabytes of data, so big data,” he says.
Instead of having people manually look through tonnes of images for presence of individuals, causalities, or of particular infrastructure, machine learning automates some of that to filter down what could be of interest to the analyst.
“It very quickly labels things to recognise this is water, this is sand, this is forest, these are buildings, etc. And it can filter out most of the uninteresting things, so you’ve got 8 terabytes of data or 100 terabytes of data and it is only letting you look at 4 or 5 per cent of that data where it looks interesting,” he says.
Machine learning also helps with mapping out the state of the environment and infrastructure post natural disaster, where it constructs a map in real time and compares it with a stored map to pin point where the damages have occurred, Zelinsky says.
Toby Walsh, AI researcher at National ICT Australia, is looking at how machine learning can apply to the organisation’s bionic eye project.
“There’s the hardware aspect – to physically connect to someone’s eye ball and can improve their eye sight when they get macular degeneration. But then, like most things, it turns into a software problem.
"You can take all the work that has been done on computer vision algorithms to try and actually improve the quality of the image you are trying to project on the back of the eye ball,” he says.
Computer vision helps with the bionic eye’s capability in spotting moving obstacles and then magnifying them so the user doesn’t run into them. Facial recognition can also be built into the bionic eye so that users are able to identify people they interact with.
“They implant electrodes on the back of the eye. The brain is so wonderfully plastic as well so it’s going adapt to the signal you put on it,” Walsh says.
Route optimisation is another machine learning project Walsh is working on at NICTA. “This is the classic travelling salesman problem. There’s approximation algorithms, local search methods that take a solution and improve it and look at ways of tweaking the solution and improve it again.”
A recent project NICTA helped work on that has turned into a startup is Foodbank Local, where it finds the most optimal route for food charities to pick up and deliver food.
The app factors in a number of variables to suggest the most efficient route, and gives users turn by turn directions of from start to finish in their journey of picking up and delivering food from supermarkets in their local area.
“If we can improve the efficiency which charities work, if we can feed more with less,” Walsh points out.
Walsh adds he is also looking at how game theory can be applied to optimisation, especially as these problems involve many stakeholders with sometimes competing interests.
“We are dealing with the fact that there is not just one person playing here; there are multiple players coming together and they may behave selfishly. So how do we design mechanisms that even if they are going to behave in selfish ways that out of that we get some optimal or good behaviours.”
One of the biggest advances in AI is IBM’s Watson supercomputer, made up of hundreds of machine learning algorithms. Jeff Welser, VP and lab director of IBM Research – Almaden, says he is trying to teach Watson when to ask questions back to the user before making its next best guess.
When going through the process of discovery, Watson sometimes comes back with three or four possible answers that are all about the same level of confidence. Having Watson figure out what information it might need to be able to differentiate its possible answers by asking the user questions is the next step, Welser says.
“It’s really more about how does the system understand what are reasonable answers for it to ask back. So Q&A’s the first step. And then there’s giving you more assistance on doing something back and forth.”
Read: Machine learning used to predict clinical response to anti-cancer drugs
Today Watson is mostly being trained to read through millions of documents for drug discovery, which means it has to understand the ins and outs of disciplines such as chemistry, biology, toxicology and compare different studies on these.
“Our drug discovery process today is very time consuming and costly. It takes hundreds of millions of dollars to make one drug. And our failure rate is over 90 per cent still, partly because a lot of today’s diseases are very non-trivial ... like cancer and multiple sclerosis, which are not very well understood,” says IBM researcher, Ying Chen.
“And the diseases themselves change. Once you make something, the disease adapts itself. So this process makes the discovery extremely difficult,” she adds.
When it comes to Watson coming back to the user with additional questions, it needs to be tuned to specific domains.
“We have some technology that is already doing reasoning and inference based on the questions that are being asked to Watson discovery advisor. But it’s a work in progress because what we’ve realised is when we apply to one particular domain there may be domain specific rules and knowledge that needs to be incorporated as Watson comes back with additional questions,” Chen says.
“We started building the system several years ago, but rapidly we realised you’ve got to give it intelligence to understand a specific area you are looking at. We need to be working hand in hand with domain experts, who really understand this field at a deep level. They are the ones who can explain what pattern is or isn’t interesting to them,” Welser adds.
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